Scheduling Approach Research Articles

Cost-aware cloud workflow scheduling using DRL and simulated annealing

Cloud workloads are highly dynamic and complex, making task scheduling in cloud computing a challenging problem. While several scheduling algorithms have been proposed in recent years, they are mainly designed to handle batch tasks and not well-suited for real-time workloads. To address this issue, researchers have started exploring the use of Deep Reinforcement Learning (DRL). However, the existing models are limited in handling independent tasks and cannot process workflows, which are prevalent in cloud computing and consist of related subtasks. In this paper, we propose SA-DQN, a scheduling approach specifically designed for real-time cloud workflows. Our approach seamlessly integrates the Simulated Annealing (SA) algorithm and Deep Q-Network (DQN) algorithm. The SA algorithm is employed to determine an optimal execution order of subtasks in a cloud server, serving as a crucial feature of the task for the neural network to learn. We provide a detailed design of our approach and show that SA-DQN outperforms existing algorithms in terms of handling real-time cloud workflows through experimental results.

A coordinated scheduling approach for task assignment and multi-agent path planning

Path finding is an essential problem in multi-agent systems, widely employed in warehousing and logistics. However, most of the current studies focus on the problem of assigning one agent with one task in a period, which may hinder the efficiency of path planning of the systems when facing multi-tasks. To address this problem, we propose a multi-layer planner, under which a co-scheduling method for multi-agent with batch tasks and path planning in a continuous workspace is proposed. In the task allocation layer of the framework, a standard Genetic Algorithm (GA) is adopted, which optimizes the allocation of task sets, and minimizes the path lengths and the probability of conflicts. Secondly, an Improved Car-Like Conflict-Based Search (ICL-CBS) algorithm is presented as the conflict resolution layer to reduce the runtime. Finally, ICL-CBS and the Spatiotemporal Hybrid-State A* (SHA*) algorithm are jointly used to determine multi-agent paths in the path planning layer. We conduct experiments and compare our method with the baseline algorithm on random obstacles and practical scenarios. The results show that our method effectively improves the efficiency of multi-task completion, and alleviates the computational burden of underlying path planning. Specifically, our method has less runtime.

Hybrid Edge-Cloud Collaborator Resource Scheduling Approach Based on Deep Reinforcement Learning and Multiobjective Optimization

Hybrid Edge-Cloud Collaborator Resource Scheduling Approach Based on Deep Reinforcement Learning and Multiobjective Optimization

Multiobjective Optimization in Distributed Industry and Environmental Sustainability: a Systematic Literature Review

With the growth of industrialization, researchers have also become increasingly concerned about environmental protection. Environmental issues have been one of the problems and one of the objectives to consider when it comes to production scaling problems, mainly minimizing energy consumption, and minimizing carbon emissions, as well as various other objectives to optimize. Measures that put pressure on organizations to pay more attention to the environment have been created, along with other measures, not only economic but also social. Good production scheduling allows organizations to be more successful in business, as it contributes to a better environment and society. Therefore, the search for processes that allow for more effective and efficient decision-making is becoming a subject of paramount importance to study. Sustainability is currently an urgent challenge for engineering and organizations. One of the ways to contribute to more sustainable manufacturing systems is the development of intelligent technologies and the sharing of manufacturing systems. This paper studies the literature on production scheduling approaches in distributed companies and their potential benefits for the environment and society, in addition to the economic benefits. In this way, the optimization of environmental, social, and economic measures in the planning and scheduling of production in extended company contexts, using approaches based on multiobjective optimization, is a primary focus of this work.

A digital twin-based decision support approach for AGV scheduling

The dynamic and complex environment affects the operational efficiency of automated guided vehicles (AGVs) at automated container terminals, in which artificial intelligent approaches are applied to address optimization problems in dynamic systems. This paper proposes a digital twin-based decision support approach to improve the efficiency of AGV scheduling service. Accordingly, the factors that affect AGV scheduling performance, including conflicts, failures, and battery constraints, are discussed first. Then, the framework and main steps of the proposed approach are described. The physical operation space is mapped into the virtual space, and both spaces keep synchronized to support the verification of solutions. A mathematical programming model and Q-learning algorithm are used to generate the AGV scheduling plan considering battery charging. Numerical experiments are conducted to demonstrate the effectiveness of the proposed approach. Comparisons of the digital twin-based approach, genetic algorithm (GA), and particle swarm optimization (PSO) are also made with different scale experiments. It appears that the proposed digital twin-based approach is superior to GA and PSO when solving small- and large-scale cases. A sensitivity analysis is performed concerning the battery utilization rate, task, and AGV number. Experimental results show that an optimal configuration of AGV and task can improve the battery utilization rate and reduce the completion time.

Introducing the Music Therapy Visual Schedule Approach (MT-ViSA): Contexts and considerations for visual design and production

Visual schedules have been used with different population groups within music therapy practice, mostly with people on the autism spectrum, however there is a paucity of focused literature on the use of these tools in the field. Drawing on a range of diverse information sources (e.g. literature, research data, practice experience), the Music Therapy Visual Schedule Approach (MT-ViSA) was developed. The MT-ViSA is a co-design approach to using visual schedules within music therapy practice. This article introduces the MT-ViSA and poses detailed questions under the four components of assessing, planning, delivering and evaluating; and visual design and production considerations for music therapists utilising a visual schedule within their work. This article suggests that a visual schedule may be of benefit to music therapy participants with different needs across the lifespan. It is anticipated that the outputs from this research will contribute to the evidence-base and practice approach of utilising visual schedules within music therapy service delivery.

Hybrid optimization approach for power scheduling with PV-battery system in smart grids

This manuscript proposes a hybrid method for the smart-grid (SG) optimization, which combines automatic demand-response (DR) shedding with load classification. The integration of the Mexican Axolotl Optimization (MAO) and Honey Badger Algorithm (HBA) constitutes the proposed hybrid approach. The HBA method improves the axolotls' updating behavior. It is commonly referred to as the Enhanced MAO (EMAO) approach. The proposed energy-management framework optimizes customer power consumption patterns to minimize carbon-emissions, electricity-costs, and peak-power-consumption. By integrating utility generation, PV-battery systems, and dynamic price signals using the EMAO approach, it reduces power consumption costs, minimizes peak-fluctuations, and lowers carbon emissions. The EMAO control-topology is rigorously evaluated through MATLAB simulations, demonstrating superior performance compared to existing optimization methods such as HGPO, PSO, and GA. The results showcase the EMAO algorithm consistently achieving the lowest cost at 310 cents, minimizing carbon emissions to 1.8 pounds, and achieving a high load classification accuracy of 98.2 %. With a moderate performance-to-cost ratio of 1.7, the EMAO algorithm excels in energy management, effectively balancing cost considerations, environmental impact, and load classification objectives. The proposed hybrid method effectively integrates DR shedding and load classification to optimize SG-operation, achieving significant improvements in cost, emissions, and load-classification accuracy compared to traditional methods.

A novel scheduling approach of stochastic cogeneration model in power system environment using improved civilized swarm search algorithm to reduce cost and carbon emission

This study explores a cogeneration system's optimal commitment and generation scheduling while considering power and heat demand uncertainty. The deterministic model corresponding to the Cogeneration Based Unit Commitment Problem (CBUCP) cannot express the uncertainties related to input data and energy demand. In the present work, the stochastic Multi-Objective Cogeneration Based Unit Commitment Problem (MO-CBUCP) aims to obtain the optimal generation schedule at minimum operational cost and emission, considering uncertainties in the heat and power demand. To deal with continuous and binary variables of MO-CBUCP and obtain an optimal solution, an effective combination of nature-inspired techniques such as Civilized Swarm Search Algorithm (CSSA) and Binary Particle Swarm Optimization (BPSO) is employed for the cogeneration-based test system. To improve the exploration ability of CSSA, the predator and seasonal effect has been incorporated, as the movement of particles is affected by weather changes. Improved CSSA effectively balances the exploration and exploitation during the optimization. The results of the proposed technique are compared with other optimization techniques to show efficacy. The comparative analysis shows that the cogeneration plant significantly affects operating cost and emission for the deterministic and stochastic models.

DAG-Order: An Order-Based Dynamic DAG Scheduling for Real-Time Networks-on-Chip

With the high-performance requirement of safety-critical real-time tasks, the platforms of many-core processors with high parallelism are widely utilized, where network-on-chip (NoC) is generally employed for inter-core communication due to its scalability and high efficiency. Unfortunately, large uncertainties are suffered on NoCs from both the overly parallel architecture and the distributed scheduling strategy (e.g., wormhole flow control), which complicates the response time upper bounds estimation (i.e., either unsafe or pessimistic). For DAG-based real-time parallel tasks, to solve this problem, we propose DAG-Order, an order-based dynamic DAG scheduling approach, which strictly guarantees NoC real-time services. First, rather than build the new analysis to fit the widely used best-effort wormhole NoC, DAG-Order is built upon a kind of advanced low-latency NoC with SLT ( S ingle-cycle L ong-range T raversal) to avoid the unpredictable parallel transmission on the shared source-destination link of wormhole NoCs. Second, DAG-Order is a non-preemptive dynamic scheduling strategy, which jointly considers communication as well as computation workloads, and fits SLT NoC. With such an order-based dynamic scheduling strategy, the provably bound safety is ensured by enforcing certain order constraints among DAG edges/vertices that eliminate the execution-timing anomaly at runtime. Third, the order constraints are further relaxed for higher average-case runtime performance without compromising bound safety. Finally, an effective heuristic algorithm seeking a proper schedule order is developed to tighten the bounds. Experiments on synthetic and realistic benchmarks demonstrate that DAG-Order performs better than the state-of-the-art related scheduling methods.

Extending Closed-Loop Control of Two-Dimensional Supersonic Boundary-Layer Instabilities Beyond a Single Operating Point

This paper investigates two control strategies to maintain low perturbation amplitudes of second Mack mode instabilities in a two-dimensional boundary layer with freestream velocity variations of ±5% around M∞=4.5: the multimodel synthesis and the gain scheduling method, both based on the structured mixed H2/H∞ synthesis to handle simultaneously, in an optimal way, the problems of nominal performance, stability, and performance robustness. The multimodel synthesis is based on a single linear-time invariant (LTI) controller optimized over multiple operating points, whereas the gain scheduling method results in a control law based on the interpolation of multiple LTI controllers designed at the various operating points. Both methods are compared with simple LTI controllers based solely on the nominal operating point in two sensor–actuator architectures: in feedforward (respectively, feedback) the estimation sensor is located upstream (respectively, downstream) of the actuator. In the feedforward case, significant performance loss is observed in off-design conditions, regardless of the method used. In the feedback case, multimodel synthesis enhances slightly performance robustness (but improvement is weak because of the wide range of freestream velocity variation), while the gain scheduling approach succeeds in strongly reducing the perturbation amplitude over the entire range of variation.

Managing Sustainable Working Hours within Participatory Working Time Scheduling for Nurses and Assistant Nurses: A Qualitative Interview Study with Managers and Staffing Assistants

Aim. To bring insights into how healthcare managers and staffing assistants work to achieve sustainable working hours within a participatory scheduling system. Background. Hospital nurses and assistant nurses often work on rotating shifts, which affects their opportunities for sleep, recovery, and work-life balance. In Sweden, a participatory scheduling approach is commonly used, where working hours are planned in collaboration between employees, managers, and staffing assistants. Influence over working hours is related to positive outcomes among shift workers. However, it also places responsibility on the employee to schedule working hours that promote health and patient safety, i.e., sustainable working hours. Accordingly, the organisation has responsibilities to support the employee in this regard. Methods. Semistructured individual interviews were conducted with 11 managers and 9 staffing assistants from four Swedish regions and analysed using thematic analysis. Results. Several key factors for achieving sustainable working hours within the context of participatory scheduling were described: distribution and clarity of responsibilities, allocating time for scheduling, establishing shared responsibility, considering fairness, fostering an individual relationship with the employee, managing dissatisfaction, providing support, clarifying guidelines for sustainable scheduling, managing inconsistencies between employee requests and sustainable working hours, and considering recovery opportunities and the competence mix on shifts. Additionally, contextual factors, such as staffing levels, working procedures, working time arrangements for night work, and technological support, were highlighted as important. Conclusion. Achieving sustainable working hours within participatory scheduling involves considering the interactions between factors at the levels of the organisation, the individual, and the technological systems. Implication for Nursing Management. Nurse managers and staffing assistants must work closely with their employees during participatory scheduling to ensure sustainable working hours. Key goals in this regard include establishing a shared responsibility, clarifying responsibilities and guidelines for sustainable scheduling, and allocating time for the scheduling process.

Enhanced planning and scheduling in building construction projects: an innovative approach to overcome scheduling challenges

This article introduces an Enhanced Planning and Scheduling (EPS) approach for complex construction projects with multiple sections, offering an innovative solution to tackle existing planning and scheduling challenges. This research presents a systematic and computational approach to optimize project duration and enhance schedule reliability and productivity. The goal is to overcome the inherent limitations of traditional scheduling methods. The proposed methods have the potential to be integrated with emerging cross-industrial project management strategies to enhance planning and scheduling processes in construction. The EPS method is executed in three stages: the enhanced Milestone, Phase, and Production schedules. Fundamental elements of this methodology encompass work areas, activities categories, labor hours estimation, and planning indicators. Drawing on combinatorial optimization concepts, this study offers industry practitioners an effective, easily-implementable solution. A case study was conducted to assess the application and effectiveness of the Milestone schedule. The results showed a 40-workday reduction in project duration compared to the traditional scheduling methods in Table 4.

A survey on energy‐efficient workflow scheduling algorithms in cloud computing

AbstractThe advancements in computing and storage capabilities of machines and their fusion with new technologies like the Internet of Thing (IoT), 5G networks, and artificial intelligence, to name a few, has resulted in a paradigm shift in the way computing is done in a cloud environment. In addition, the ever‐increasing user demand for cloud services and resources has resulted in cloud service providers (CSPs) expanding the scale of their data center facilities. This has increased energy consumption leading to more carbon dioxide emission levels. Hence, it becomes all the more important to design scheduling algorithms that optimize the use of cloud resources with minimum energy consumption. This paper surveys state‐of‐the‐art algorithms for scheduling workflow tasks to cloud resources with a focus on reducing energy consumption. For this, we categorize different workflow scheduling algorithms based on the scheduling approaches used and provide an analytical discussion of the algorithms covered in the paper. Further, we provide a detailed classification of different energy‐efficient strategies used by CSPs for energy saving in data centers. Finally, we describe some of the popular real‐world workflow applications as well as highlight important emerging trends and open issues in cloud computing for future research directions.

A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter

Supply chain management is a vital part of ensuring service quality and production efficiency in industrial applications. With the development of cloud computing and data intelligence in modern industries, datacenters have become an important basic support for intelligent applications. However, the increase in the number and complexity of tasks makes datacenters face increasingly heavy task processing demands. Therefore, there are problems of long task completion time and long response time in the task scheduling process of the data center. A multi-swarm particle swarm optimization task scheduling approach based on load balancing is proposed in this paper, aiming to reduce the maximum completion time and response time in task scheduling. The proposed approach improves the fitness evaluation function of particle swarms to facilitate load balancing. The new adaptive inertia weight and initialization method design can improve the search efficiency and convergence speed of particles. Meanwhile, the multi-swarm design can avoid the problem of particles falling into local optimum as much as possible. Finally, the proposed algorithm is verified experimentally using the task dataset released by Alibaba datacenter, and compared with other benchmark algorithms. The results show that the proposed algorithm can improve the task scheduling performance of the datacenter in supply chain management when dealing with different workloads and changes in the number of elastic machines.

Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads

Large models such as GPT-3 and ChatGPT have transformed deep learning (DL), powering applications that have captured the public's imagination. Such models must be trained on multiple GPUs due to their size and computational load, driving the development of a bevy of "model parallelism" techniques and tools. Navigating such parallelism choices, however, is a new burden for DL users such as data scientists, domain scientists, etc., who may lack the necessary systems knowhow. The need for model selection , which leads to many models to train due to hyper-parameter tuning or layer-wise finetuning, compounds the situation with two more burdens: resource apportioning and scheduling. In this work, we unify these three burdens by formalizing them as a joint problem that we call SPASE: Select a Parallelism, Allocate resources, and Schedule. We propose a new information system architecture to tackle the SPASE problem holistically, exploiting the performance opportunities presented by joint optimization. We devise an extensible template for existing parallelism schemes and combine it with an automated empirical profiler for runtime estimation. We then formulate SPASE as an MILP. We find that direct use of an MILP-solver is significantly more effective than several baseline heuristics. We optimize the system runtime further with an introspective scheduling approach. We implement all these techniques into a new data system we call Saturn. Experiments with benchmark DL workloads show that Saturn achieves 39-49% lower model selection runtimes than current DL practice.

Climatological approaches of irrigation scheduling for growing tomato crop under drip irrigation in sub-tropical region of Punjab

A field experiment was conducted at Lovely Professional University, Phagwara, Punjab for two years (2022 and 2023) to study the response of tomato crop to drip irrigation scheduling based on climatological approach viz. Penman-Monteith, Blaney-Criddle and pan evaporation. Result revealed that, the all treatments of irrigation scheduling were found feasible for optimizing tomato yield (30.8 to 44.6 t ha-1), water saving (27 to 50.7%) and water use efficiency (1.31 to 1.61 t ha-1-cm) under drip irrigation over soil moisture depletion approach. In selected region the average daily ET0 (4.4 to 9 mm day-1) and ETC (2.5 to 10.8 mm day-1) varies with different growth stages and results varying crop water demand of tomato. This water demand can successfully meet out by applying water at 100 % ET0 based on Penman-Monteith method with significant crop yield (44.6 t ha-1) and water use efficiency (1.33 t ha-1-cm). Correlation analysis indicated that, in case of other regions where the availability of weather parameters will be limited for irrigation scheduling, the farmer could irrigate their tomato crop at 90% ET0 based on daily pan evaporation method under drip irrigation. In selected region, the Blaney-Criddle method was found ineffective and shows under and overestimated values of daily ETo during mid stage and late stage which gives more water saving (up to 51%) but reduces significant tomato yield over Pan Evaporation method.

A chameleon and remora search optimization algorithm for handling task scheduling uncertainty problem in cloud computing

Task scheduling in cloud computing is responsible for serving the user requirements. The scheduling strategy must handle the problems of high load over virtual machines (VMs), high-cost consumption and lengthier scheduling time effectively. The greatest challenge in the cloud computing environment is achieving the intended outcome of task scheduling under the uncertain user request demands as it is responsible for assigning specific resources to requests for achieving effective task completion. However, most of the task scheduling approaches contributed to the literature mainly focused on the design and development of scheduling algorithms but ignored to explore the impact of uncertain factors such as millions of instructions per second (MIPS) and network bandwidth during the scheduling process. In this paper, A Chameleon and Remora Search Optimization Algorithm (CRSOA) is proposed for achieving efficient scheduling process by exploring the impact of MIPS and network bandwidth which directly affects the virtual machine (VM) performance. Further the work includes the uncertainty factors of task completion rate, load balance, scheduling cost and makespan in a simultaneous manner during the process of scheduling. It is formulated a multi-objective cloud task scheduling optimization model by integrating the merits of Chameleon Search Algorithm (CSA) and Remora Search Optimization Algorithm (RSOA) using a greedy methodology for simulating the real cloud computing task scheduling process. The simulation results evidently confirmed that the proposed CRSOA approach is minimizing the completion time and effective in handling the load balancing between the available VMs against other competitive metaheuristic task scheduling algorithms. The experimental investigation of this CRSOA confirmed its predominance in minimizing the makespan by 18.96%, cost by 22.18%, and degree of imbalance by 20.54%, compared to the baseline approaches with different number of tasks and VMs.

Implementing gain scheduling approach to control large-scale production of diethyl oxalate in a catalytic fixed bed multi-tubular reactor

In the large-scale production of Diethyl Oxalate (DEO), numerous control and optimization challenges are faced. These issues are targeted in this study by implementing a gain scheduling approach to control a catalytic fixed bed multi-tubular reactor designed for DEO production. A novel inferential modeling method, developed using Python and response surface methodology, was applied to simulate the dynamics of the reactor, and the inlet coolant temperature was identified as the most influential parameter. An inferential control system was implemented that utilized gain scheduling for Proportional Integral Derivative (PID) controllers. The results showed that the proposed PID controller gains offered fast and stable responses, even at high conversion setpoints. However, minimization of response overshoot at high amplitudes of step changes proved challenging, leading to a decrease in conversion estimator accuracy. The use of a quadratic model from response surface methodology for the regression of the inferential estimator was proposed, showing improved prediction accuracy over the linear model. These findings provide valuable insights and guide future optimization in the design and operation of reactors for large-scale DEO production.

Durable Clinical Benefits of Idasanutlin Therapy in Hydroxyurea-Refractory Polycythemia Vera

Background: In phase 1 (NCT02407080) and phase 2 (NCT03287245) clinical trials, idasanutlin, a human double minute 2 homolog (HDM2) antagonist, demonstrated robust clinical activity and rapid reduction in JAK2V617F variant allele frequencies (VAF) in patients with hydroxyurea-intolerant or refractory polycythemia vera (PV). Despite promising disease-targeted clinical efficacy, high rates of discontinuation due to toxicity, and concerns regarding the potential clinical implications of transient TP53 mutations had dampened the enthusiasm for pursuing further development of this class of drugs in PV patients. 1,2,3 Given the significant reduction in JAK2V617F VAF that was documented in the clinical trials, we postulated that idasanutlin may have durable clinical effects in terms of reduction in phlebotomy requirement, splenomegaly, and disease progression, as well as questioned the significance of the transient increments in TP53 mutations. To assess the long-term implications of this therapy on PV patients, we examined the longer-term effects of idasanutlin administration. Methods: We retrospectively evaluated the long-term outcomes of patients enrolled in the phase 1 and 2 clinical trials and used descriptive statistical analysis to evaluate a change in phlebotomy requirements, change in spleen size, and JAK2V617F VAF. Results: Seventy-six percent (29/38) of the patients enrolled in the phase 1 and phase 2 studies continued to be followed at the participating institutions after the end of trial (EOT) visit (Table 1). Median follow-up after EOT to last follow up visit was 40 months (range XX-XX months). There was no increase in 8-week phlebotomy requirements when comparing 8-week phlebotomy requirement prior to EOT to pre-specified time-points of 1, 3, 6, 12, 18, 24, 36 months after idasanutlin discontinuation (P=.50). Six of 29 (21%) participants remained off PV-directed therapy for the duration of follow up. Therapies initiated in the remainder of the patients are listed in Table 1, four of whom received either combination or sequential therapies. Reduction in splenomegaly with idasanutlin was durable with no observed increase in spleen size by palpation from EOT through the 36-month follow up visit (P=.51). Change in hematologic profile of patients throughout follow up is shown in Figure 1 (median, range). There were four (13.7%) documented cases of disease progression. Three progressed to myelofibrosis (MF), one to blast phase (BP). Two participants that progressed to MF went on to receive allogeneic hematopoietic cell transplant. Of those who progressed, median time to progression was 17.5 months (range 14-24 months) after treatment discontinuation. Of those that progressed, mutations by next generation sequencing were noted in JAK2 (4), TET2 (2), DNMT3A (1), SF3B1 (1), and ASXL1 (1). None had a known TP53 mutation at the time of disease progression. There were no documented cases of thrombotic events, or hemorrhagic events within the follow-up period. Changes in JAK2V617F VAF after treatment discontinuation will be presented at the meeting. Conclusion: HDM2 antagonist therapy represents a rational and highly effective treatment approach in PV. The durable effects of idasanutlin on phlebotomy requirements and splenomegaly persist even after treatment discontinuation. Additionally, the lack of appearance of TP53 mutations or loss of TP53 in those who progress suggest that the previously-described transient TP53 mutations do not have long-term clinical implications. Alternative dose and scheduling approaches that are more tolerable may be warranted to safely maintain long-term PV disease control. 1 Mascarenhas J, Passamonti F, Burbury K, et al. The MDM2 antagonist idasanutlin in patients with polycythemia vera: results from a single-arm phase 2 study. Blood Advances. 2022;6(4):1162-1174. doi:10.1182/bloodadvances.2021006043 2 Mascarenhas J, Lu M, Kosiorek H, et al. Oral idasanutlin in patients with polycythemia vera. Blood. 08 08 2019;134(6):525-533. doi:10.1182/blood.2018893545 3 Marcellino BK, Farnoud N, Cassinat B, et al. Transient expansion of TP53 mutated clones in polycythemia vera patients treated with idasanutlin. Blood Advances. 2020;4(22):5735-5744. doi:10.1182/bloodadvances.2020002379

Optimizing Toll Collector Scheduling using Goal Programming

The importance of providing an effective shift work schedule cannot be overstated, particularly when considering the continuous 24-hour service requirement. Each employee has their own schedule preferences, and while some positions offer just one or two shifts, others encompass three separate scheduling periods within a day. The issue arises when these varying schedules lead to disparities among workers of toll collectors in terms of overall time spent, creating an inequitable situation. Unfortunately, the objectives of the Bentong Toll Plaza and the preferences of the toll collectors have not been in harmony, which could potentially impact the performance and worklife balance of the toll collectors. The ongoing challenge faced by managers is the selection of the most suitable shift schedule. Thus, this study aims to introduce a new cyclic scheduling approach to streamline the task of creating schedules at Bentong Toll Plaza. Goad programming is applied to meet the requirements of four rigid constraints and three flexible constraints. The initial schedule is used for the first 15 days and then systematically rotated for another 15 days until all toll collectors have experienced all 15 schedule sets. This scheduling process was facilitated through the utilization of Lingo software, resulting in an optimized 15-day shift schedule for toll collectors. The third goal was achieved, which was that each toll collector was assigned the same amount of work. However, the first and second goals are not entirely met, but this has a minimal impact on the toll collectors due to the schedule rotation. In conclusion, it has been proven that the generated schedule pattern provides a better schedule in terms of providing each toll collector with the same total number of shifts and off days.